CovNav is a training‑free framework that uses Conformal Prediction to decide when an embodied agent should switch from fast, low‑latency LLM reasoning to slower, more accurate chain‑of‑thought reasoning during object‑goal navigation. By treating each spatial query as a binary decision and calibrating the fast LLM’s token probabilities, CovNav escalates only ambiguous queries to the slow mode, achieving higher success rates on Gibson and HM3D while invoking slow reasoning for just 12.4% of queries. Real‑robot tests on a TurtleBot 4 confirm the method’s practical benefits.
arXiv:2511. 15669v3 Announce Type: replace-cross Abstract: Does Chain-of-Thought (CoT) reasoning genuinely improve Vision Language Action (VLA) models, or does it merely add overhead?
AnchorVLN is an open‑vocabulary vision‑language navigation system that separates semantic proposals from geometric metrics. It uses a VLM to generate semantics while a geometry module supplies reliable metric quantities such as range and bearing, all within a Model Context Protocol server. The system achieves 64.4% on instruction following and improves object‑reference accuracy, reducing median center error from 3.37 m to 2.48 m.
arXiv:2608. 11739v1 Announce Type: cross Abstract: The prevailing recipe for Vision-Language-Action (VLA) models couples a pretrained VLM with a separately trained flow-matching action expert.
The paper introduces ARC, a reasoning recipe that enhances zero‑shot performance of robot foundation models (RFMs) without additional data or large‑scale training. ARC combines a reasoning trace grounded in the robot’s next action, an automatic labeling pipeline to generate these traces from existing demonstrations, and a strategy to adapt pretrained RFMs to use the traces for control. Applying ARC yields significant gains, including up to 50 percentage points on RoboLab-Reasoning-50 and an 82.2‑point improvement in task success for the π_{0.5} model on real robots.
As Multimodal Large Language Models (MLLMs) are increasingly deployed in decision-critical pipelines such as robotics, embodied AI, and safety monitoring, the opacity of their spatial judgments limits operator trust and auditability. MLLMs demonstrate strong reasoning but often struggle with fine-grained spatial understanding and object hallucination.
arXiv:2610.14541v1 Announce Type: cross Abstract: Emerging strong Vision-Language Models (VLMs) have advanced navigation foundation models. However, these models still have unsatisfactory performance...
arXiv:2603. 18624v2 Announce Type: replace-cross Abstract: Zero-shot object-goal navigation (ZSON) requires navigating unknown environments to find a target object without task-specific training.
arXiv:2608.12743v3 Announce Type: replace Abstract: Spatial intelligence is becoming a foundation for embodied agents, robotic planning, and multimodal assistants. To improve the spatial reasoning ab...
arXiv:2608. 12743v1 Announce Type: new Abstract: Spatial intelligence is becoming a foundation for embodied agents, robotic planning, and multimodal assistants.
arXiv:2606. 00229v1 Announce Type: cross Abstract: Natural language is a powerful reasoning medium for language and vision-language models, but it is mismatched to the granularity of continuous control.
arXiv:2607. 04681v1 Announce Type: cross Abstract: Embodied Chain-of-Thought has emerged as a promising mechanism to enhance robot decision-making and interpretability in black-box Vision-Language Action (VLA) models.
Think3D introduces a framework that endows Vision‑Language Models with interactive 3D chain‑of‑thought reasoning by integrating 3D manipulation tools for active spatial exploration. The approach improves performance on benchmarks such as BLINK Multi‑view, MindCube‑1K, and VSI‑Bench‑Tiny for proprietary models like GPT‑4.1 and Gemini 2.5 Pro, and a reinforcement‑learning variant, Think3D‑RL, enables open‑weight models such as Qwen3‑VL‑4B to autonomously learn effective 3D exploration strategies, yielding tool‑use patterns comparable to stronger models and turning a performance drop on MindCube‑1K into a substantial improvement.